A Real-Time Multiobjective Optimization Algorithm for Discovering Driving Strategies

Erik Dovgan (), Matjaž Gams () and Bogdan Filipič ()
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Erik Dovgan: Department of Intelligent Systems, Jožef Stefan Institute, SI-1000 Ljubljana, Slovenia; Faculty of Computer and Information Science, University of Ljubljana, SI-1000 Ljubljana, Slovenia
Matjaž Gams: Department of Intelligent Systems, Jožef Stefan Institute, SI-1000 Ljubljana, Slovenia; Jožef Stefan International Postgraduate School, SI-1000 Ljubljana, Slovenia
Bogdan Filipič: Department of Intelligent Systems, Jožef Stefan Institute, SI-1000 Ljubljana, Slovenia; Jožef Stefan International Postgraduate School, SI-1000 Ljubljana, Slovenia

Transportation Science, 2019, vol. 53, issue 3, 695-707

Abstract: Vehicle driving consists of selecting and applying the best control actions in real time to optimize several objectives such as the traveling time and the fuel consumption. Because more than one objective is optimized, this problem can be solved using multiobjective optimization techniques. However, the existing optimization algorithms mostly combine objectives into a weighted-sum cost function and solve the corresponding single-objective problem. To test the multiobjective approach, we developed the multiobjective optimization algorithm for discovering driving strategies (MODS) that searches for the best driving strategies by taking into account the entire route. Although this algorithm, on average, outperforms existing single-objective algorithms for discovering driving strategies, it has a drawback, namely, it cannot be used for real-time optimization because of its time complexity. To overcome this shortage, we redesigned the MODS algorithm, obtaining the real-time multiobjective optimization algorithm for discovering driving strategies (MODS-RT). The MODS-RT algorithm was tested on data from real-world routes and compared with MODS and traditional single-objective algorithms for discovering driving strategies. Although MODS-RT found worse driving strategies than MODS, it found better driving strategies than the single-objective algorithms, thus proving that the multiobjective approach can be effectively adapted for real-time discovery of driving strategies.

Keywords: real-time optimization; driving strategy; traveling time; fuel consumption; multiobjective optimization; black-box approach (search for similar items in EconPapers)
Date: 2019
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